OpenRaiser/Intern-Atlas huggingface dataset:
Context-Aware Service Utilisation in the Clouds and Energy Conservation
Venue: arXiv cs.DC 2012
Year: 2012

Ubiquitous computing environments are characterised by smart, interconnected artefacts embedded in our physical world that are projected to provide useful services to human inhabitants unobtrusively. Mobile devices are becoming the primary tools of human interaction with these embedded artefacts and utilisation of services available in smart computing environments such as clouds. Advancements in capabilities of mobile devices allow a number of user and environment related context consumers to be hosted on these devices. Without a coordinating component, these context consumers and providers are a potential burden on device resources; specifically the effect of uncoordinated computation and communication with cloud-enabled services can negatively impact the battery life. Therefore energy conservation is a major concern in realising the collaboration and utilisation of mobile device based context-aware applications and cloud based services. This paper presents the concept of a context-brokering component to aid in coordination and communication of context information between mobile devices and services deployed in a cloud infrastructure. A prototype context broker is experimentally analysed for effects on energy conservation when accessing and coordinating with cloud services on a smart device, with results signifying reduction in energy consumption.
A Framework Studio for Component Reusability
Venue: arXiv cs.SE 2012
Year: 2012

The deployment of a software product requires considerable amount of time and effort. In order to increase the productivity of the software products, reusability strategies were proposed in the literature. However effective reuse is still a challenging issue. This paper presents a framework studio for effective components reusability which provides the selection of components from framework studio and generation of source code based on stakeholders needs. The framework studio is implemented using swings which are integrated onto the Net Beans IDE which help in faster generation of the source code.
Fréchet Distance Revisited and Extended
Venue: arXiv cs.CG 2012
Year: 2012

Given two simplicial complexes in R^d, and start and end vertices in each complex, we show how to compute curves (in each complex) between these vertices, such that the Fréchet distance between these curves is minimized. As a polygonal curve is a complex, this generalizes the regular notion of weak Fréchet distance between curves. We also generalize the algorithm to handle an input of k simplicial complexes. Using this new algorithm we can solve a slew of new problems, from computing a mean curve for a given collection of curves, to various motion planning problems. Additionally, we show that for the mean curve problem, when the k input curves are c-packed, one can (1+epsilon)-approximate the mean curve in near linear time, for fixed k and epsilon. Additionally, we present an algorithm for computing the strong Fréchet distance between two curves, which is simpler than previous algorithms, and avoids using parametric search.
Fractional Order Phase Shaper Design with Routh's Criterion for Iso-damped Control System
Venue: arXiv cs.SY 2012
Year: 2012

Phase curve of an open loop system is flat in nature if the derivative of phase with respect to frequency is zero. With a flat phase curve, the corresponding closed-loop system exhibits an iso-damped property i.e. maintains constant overshoot with the change of gain and with other parametric variations. In recent past application, fractional order (FO) phase shapers have been proposed by contemporary researchers to achieve enhanced parametric robustness. In this paper, a simple Routh tabulation based methodology is proposed to design an appropriate FO phase shaper to achieve phase flattening in a control loop, comprising a system, controlled by a classical PID controller. The method is demonstrated using MATLAB simulation of a second order DC motor plant and also a first order with time delay system.
Improved Model Reduction and Tuning of Fractional Order PIλDμ Controllers for Analytical Rule Extraction with Genetic Programming
Venue: arXiv cs.SY 2012
Year: 2012

Genetic Algorithm (GA) has been used in this paper for a new approach of sub-optimal model reduction in the Nyquist plane and optimal time domain tuning of PID and fractional order (FO) PIλDμ controllers. Simulation studies show that the Nyquist based new model reduction technique outperforms the conventional H2 norm based reduced parameter modeling technique. With the tuned controller parameters and reduced order model parameter data-set, optimum tuning rules have been developed with a test-bench of higher order processes via Genetic Programming (GP). The GP performs a symbolic regression on the reduced process parameters to evolve a tuning rule which provides the best analytical expression to map the data. The tuning rules are developed for a minimum time domain integral performance index described by weighted sum of error index and controller effort. From the reported Pareto optimal front of GP based optimal rule extraction technique a trade-off can be made between the complexity of the tuning formulae and the control performance. The efficacy of the single-gene and multi-gene GP based tuning rules has been compared with original GA based control performance for the PID and PIλDμ controllers, handling four different class of representative higher order processes. These rules are very useful for process control engineers as they inherit the power of the GA based tuning methodology, but can be easily calculated without the requirement for running the computationally intensive GA every time. Three dimensional plots of the required variation in PID/FOPID controller parameters with reduced process parameters have been shown as a guideline for the operator. Parametric robustness of the reported GP based tuning rules has also been shown with credible simulation examples.
Genetic Algorithm Based Improved Sub-Optimal Model Reduction in Nyquist Plane for Optimal Tuning Rule Extraction of PID and PIλDμ Controllers via Genetic Programming
Venue: arXiv cs.SY 2012
Year: 2012

Genetic Algorithm (GA) has been used in this paper for a new Nyquist based sub-optimal model reduction and optimal time domain tuning of PID and fractional order (FO) PIλDμ controllers. Comparative studies show that the new model reduction technique outperforms the conventional H2-norm based reduced order modeling techniques. Optimum tuning rule has been developed next with a test-bench of higher order processes via Genetic Programming (GP) with minimum value of weighted integral error index and control signal. From the Pareto optimal front which is a trade-off between the complexity of the formulae and control performance, an efficient set of tuning rules has been generated for time domain optimal PID and PIλDμ controllers.
Divide-and-Conquer Method for L1 Norm Matrix Factorization in the Presence of Outliers and Missing Data
Venue: arXiv cs.NA 2012
Year: 2012

The low-rank matrix factorization as a L1 norm minimization problem has recently attracted much attention due to its intrinsic robustness to the presence of outliers and missing data. In this paper, we propose a new method, called the divide-and-conquer method, for solving this problem. The main idea is to break the original problem into a series of smallest possible sub-problems, each involving only unique scalar parameter. Each of these subproblems is proved to be convex and has closed-form solution. By recursively optimizing these small problems in an analytical way, efficient algorithm, entirely avoiding the time-consuming numerical optimization as an inner loop, for solving the original problem can naturally be constructed. The computational complexity of the proposed algorithm is approximately linear in both data size and dimensionality, making it possible to handle large-scale L1 norm matrix factorization problems. The algorithm is also theoretically proved to be convergent. Based on a series of experiment results, it is substantiated that our method always achieves better results than the current state-of-the-art methods on $L1$ matrix factorization calculation in both computational time and accuracy, especially on large-scale applications such as face recognition and structure from motion.
Hierarchical Identity-Based Lossy Trapdoor Functions
Venue: arXiv cs.CR 2012
Year: 2012

Lossy trapdoor functions, introduced by Peikert and Waters (STOC'08), have received a lot of attention in the last years, because of their wide range of applications in theoretical cryptography. The notion has been recently extended to the identity-based setting by Bellare et al. (Eurocrypt'12). We provide one more step in this direction, by considering the notion of hierarchical identity-based (lossy) trapdoor functions (HIB-TDFs). Hierarchical identity-based cryptography has proved very useful both for practical applications and to establish theoretical relations with other cryptographic primitives. The notion of security for IB-TDFs put forward by Bellare et al. easily extends to the hierarchical scenario, but an (H)IB-TDF secure in this sense is not known to generically imply other related primitives with security against adaptive-id adversaries, not even IND-ID-CPA secure encryption. Our first contribution is to define a new security property for (H)IB-TDFs. We show that functions satisfying this property imply secure cryptographic primitives in the adaptive identity-based setting: these include encryption schemes with semantic security under chosen-plaintext attacks, deterministic encryption schemes, and (non-adaptive) hedged encryption schemes that maintain some security when messages are encrypted using randomness of poor quality. Then, we describe the first pairing-based HIB-TDF realization. Our HIB-TDF construction is based on techniques that differ from those of Bellare et al. in that it uses a hierarchical predicate encryption scheme as a key ingredient. The resulting HIB-TDF is proved to satisfy the new security definition, against either selective or, for hierarchies of constant depth, adaptive adversaries.
Synthesising Graphical Theories
Venue: arXiv cs.AI 2012
Year: 2012

In recent years, diagrammatic languages have been shown to be a powerful and expressive tool for reasoning about physical, logical, and semantic processes represented as morphisms in a monoidal category. In particular, categorical quantum mechanics, or "Quantum Picturalism", aims to turn concrete features of quantum theory into abstract structural properties, expressed in the form of diagrammatic identities. One way we search for these properties is to start with a concrete model (e.g. a set of linear maps or finite relations) and start composing generators into diagrams and looking for graphical identities. Naively, we could automate this procedure by enumerating all diagrams up to a given size and check for equalities, but this is intractable in practice because it produces far too many equations. Luckily, many of these identities are not primitive, but rather derivable from simpler ones. In 2010, Johansson, Dixon, and Bundy developed a technique called conjecture synthesis for automatically generating conjectured term equations to feed into an inductive theorem prover. In this extended abstract, we adapt this technique to diagrammatic theories, expressed as graph rewrite systems, and demonstrate its application by synthesising a graphical theory for studying entangled quantum states.
SpeechJammer: A System Utilizing Artificial Speech Disturbance with Delayed Auditory Feedback
Venue: arXiv cs.HC 2012
Year: 2012

In this paper we report on a system, "SpeechJammer", which can be used to disturb people's speech. In general, human speech is jammed by giving back to the speakers their own utterances at a delay of a few hundred milliseconds. This effect can disturb people without any physical discomfort, and disappears immediately by stop speaking. Furthermore, this effect does not involve anyone but the speaker. We utilize this phenomenon and implemented two prototype versions by combining a direction-sensitive microphone and a direction-sensitive speaker, enabling the speech of a specific person to be disturbed. We discuss practical application scenarios of the system, such as facilitating and controlling discussions. Finally, we argue what system parameters should be examined in detail in future formal studies based on the lessons learned from our preliminary study.
Delay-limited Source and Channel Coding of Quasi-Stationary Sources over Block Fading Channels: Design and Scaling Laws
Venue: arXiv cs.IT 2012
Year: 2012

In this paper, delay-limited transmission of quasi-stationary sources over block fading channels are considered. Considering distortion outage probability as the performance measure, two source and channel coding schemes with power adaptive transmission are presented. The first one is optimized for fixed rate transmission, and hence enjoys simplicity of implementation. The second one is a high performance scheme, which also benefits from optimized rate adaptation with respect to source and channel states. In high SNR regime, the performance scaling laws in terms of outage distortion exponent and asymptotic outage distortion gain are derived, where two schemes with fixed transmission power and adaptive or optimized fixed rates are considered as benchmarks for comparisons. Various analytical and numerical results are provided which demonstrate a superior performance for source and channel optimized rate and power adaptive scheme. It is also observed that from a distortion outage perspective, the fixed rate adaptive power scheme substantially outperforms an adaptive rate fixed power scheme for delay-limited transmission of quasi-stationary sources over wireless block fading channels. The effect of the characteristics of the quasi-stationary source on performance, and the implication of the results for transmission of stationary sources are also investigated.
Confusion Matrix Stability Bounds for Multiclass Classification
Venue: arXiv cs.LG 2012
Year: 2012

In this paper, we provide new theoretical results on the generalization properties of learning algorithms for multiclass classification problems. The originality of our work is that we propose to use the confusion matrix of a classifier as a measure of its quality; our contribution is in the line of work which attempts to set up and study the statistical properties of new evaluation measures such as, e.g. ROC curves. In the confusion-based learning framework we propose, we claim that a targetted objective is to minimize the size of the confusion matrix C, measured through its operator norm ||C||. We derive generalization bounds on the (size of the) confusion matrix in an extended framework of uniform stability, adapted to the case of matrix valued loss. Pivotal to our study is a very recent matrix concentration inequality that generalizes McDiarmid's inequality. As an illustration of the relevance of our theoretical results, we show how two SVM learning procedures can be proved to be confusion-friendly. To the best of our knowledge, the present paper is the first that focuses on the confusion matrix from a theoretical point of view.
Realisation d'un systeme de reconnaissance automatique de la parole arabe base sur CMU Sphinx
Venue: arXiv cs.CL 2012
Year: 2012

This paper presents the continuation of the work completed by Satori and all. [SCH07] by the realization of an automatic speech recognition system (ASR) for Arabic language based SPHINX 4 system. The previous work was limited to the recognition of the first ten digits, whereas the present work is a remarkable projection consisting in continuous Arabic speech recognition with a rate of recognition of surroundings 96%.
Classification of poset-block spaces admitting MacWilliams-type identity
Venue: arXiv cs.IT 2012
Year: 2012

In this work we prove that a poset-block space admits a MacWilliams-type identity if and only if the poset is hierarchical and at any level of the poset, all the blocks have the same dimension. When the poset-block admits the MacWilliams-type identity we explicit the relation between the weight enumerators of a code and its dual.
Search versus Decision for Election Manipulation Problems
Venue: arXiv cs.GT 2012
Year: 2012

Most theoretical definitions about the complexity of manipulating elections focus on the decision problem of recognizing which instances can be successfully manipulated, rather than the search problem of finding the successful manipulative actions. Since the latter is a far more natural goal for manipulators, that definitional focus may be misguided if these two complexities can differ. Our main result is that they probably do differ: If integer factoring is hard, then for election manipulation, election bribery, and some types of election control, there are election systems for which recognizing which instances can be successfully manipulated is in polynomial time but producing the successful manipulations cannot be done in polynomial time.
On the Capacity of Rate-Adaptive Packetized Wireless Communication Links under Jamming
Venue: arXiv cs.IT 2012
Year: 2012

We formulate the interaction between the communicating nodes and an adversary within a game-theoretic context. We show that earlier information-theoretic capacity results for a jammed channel correspond to a pure Nash Equilibrium (NE). However, when both players are allowed to randomize their actions (i.e., coding rate and jamming power) new mixed Nash equilibria appear with surprising properties. We show the existence of a threshold ($J_{TH}$) such that if the jammer average power exceeds $J_{TH}$, the channel capacity at the NE is the same as if the jammer was using its maximum allowable power, $J_{Max}$, all the time. This indicates that randomization significantly advantages powerful jammers. We also show how the NE strategies can be derived, and we provide very simple (e.g., semi-uniform) approximations to the optimal communication and jamming strategies. Such strategies are very simple to implement in current hardware and software.
Faceted Semantic Search for Personalized Social Search
Venue: arXiv cs.IR 2012
Year: 2012

Actual social networks (like Facebook, Twitter, Linkedin, ...) need to deal with vagueness on ontological indeterminacy. In this paper is analyzed the prototyping of a faceted semantic search for personalized social search using the "joint meaning" in a community environment. User researches in a "collaborative" environment defined by folksonomies can be supported by the most common features on the faceted semantic search. A solution for the context-aware personalized search is based on "joint meaning" understood as a joint construal of the creators of the contents and the user of the contents using the faced taxonomy with the Semantic Web. A proof-of concept prototype shows how the proposed methodological approach can also be applied to existing presentation components, built with different languages and/or component technologies.
LP-based Covering Games with Low Price of Anarchy
Venue: arXiv cs.GT 2012
Year: 2012

We present a new class of vertex cover and set cover games. The price of anarchy bounds match the best known constant factor approximation guarantees for the centralized optimization problems for linear and also for submodular costs -- in contrast to all previously studied covering games, where the price of anarchy cannot be bounded by a constant (e.g. [6, 7, 11, 5, 2]). In particular, we describe a vertex cover game with a price of anarchy of 2. The rules of the games capture the structure of the linear programming relaxations of the underlying optimization problems, and our bounds are established by analyzing these relaxations. Furthermore, for linear costs we exhibit linear time best response dynamics that converge to these almost optimal Nash equilibria. These dynamics mimic the classical greedy approximation algorithm of Bar-Yehuda and Even [3].
Pushing the Boundaries of Crowd-enabled Databases with Query-driven Schema Expansion
Venue: arXiv cs.DB 2012
Year: 2012

By incorporating human workers into the query execution process crowd-enabled databases facilitate intelligent, social capabilities like completing missing data at query time or performing cognitive operators. But despite all their flexibility, crowd-enabled databases still maintain rigid schemas. In this paper, we extend crowd-enabled databases by flexible query-driven schema expansion, allowing the addition of new attributes to the database at query time. However, the number of crowd-sourced mini-tasks to fill in missing values may often be prohibitively large and the resulting data quality is doubtful. Instead of simple crowd-sourcing to obtain all values individually, we leverage the user-generated data found in the Social Web: By exploiting user ratings we build perceptual spaces, i.e., highly-compressed representations of opinions, impressions, and perceptions of large numbers of users. Using few training samples obtained by expert crowd sourcing, we then can extract all missing data automatically from the perceptual space with high quality and at low costs. Extensive experiments show that our approach can boost both performance and quality of crowd-enabled databases, while also providing the flexibility to expand schemas in a query-driven fashion.
Application of Gist SVM in Cancer Detection
Venue: arXiv cs.LG 2012
Year: 2012

In this paper, we study the application of GIST SVM in disease prediction (detection of cancer). Pattern classification problems can be effectively solved by Support vector machines. Here we propose a classifier which can differentiate patients having benign and malignant cancer cells. To improve the accuracy of classification, we propose to determine the optimal size of the training set and perform feature selection. To find the optimal size of the training set, different sizes of training sets are experimented and the one with highest classification rate is selected. The optimal features are selected through their F-Scores.
High-Performance Distributed Multi-Model / Multi-Kernel Simulations: A Case-Study in Jungle Computing
Venue: arXiv cs.DC 2012
Year: 2012

High-performance scientific applications require more and more compute power. The concurrent use of multiple distributed compute resources is vital for making scientific progress. The resulting distributed system, a so-called Jungle Computing System, is both highly heterogeneous and hierarchical, potentially consisting of grids, clouds, stand-alone machines, clusters, desktop grids, mobile devices, and supercomputers, possibly with accelerators such as GPUs. One striking example of applications that can benefit greatly of Jungle Computing Systems are Multi-Model / Multi-Kernel simulations. In these simulations, multiple models, possibly implemented using different techniques and programming models, are coupled into a single simulation of a physical system. Examples include the domain of computational astrophysics and climate modeling. In this paper we investigate the use of Jungle Computing Systems for such Multi-Model / Multi-Kernel simulations. We make use of the software developed in the Ibis project, which addresses many of the problems faced when running applications on Jungle Computing Systems. We create a prototype Jungle-aware version of AMUSE, an astrophysical simulation framework. We show preliminary experiments with the resulting system, using clusters, grids, stand-alone machines, and GPUs.